Totals

Actblue

actblue_contribs %>% 
  group_by(ga_candidate) %>% 
  summarise(total_dollars = sum(contribution_amount), avg_amount = mean(contribution_amount), median_amount = median(contribution_amount))
ga_candidate total_dollars avg_amount median_amount
OSSOFF 55766107 46.81377 20.0
WARNOCK 58220850 45.15162 17.5

WinRed

winred_contribs %>% 
  group_by(ga_candidate) %>% 
  summarise(total_dollars = sum(contribution_amount), avg_amount = mean(contribution_amount), median_amount = median(contribution_amount))
ga_candidate total_dollars avg_amount median_amount
LOEFFLER 26995653 55.57657 25
PERDUE 28593871 59.58050 25

In-State vs. Out-of-State

Party Totals

Dem overall totals

dem_inout <- actblue_contribs %>% 
  group_by(in_out_state) %>% 
  summarise(total_dollars = sum(contribution_amount)) %>% 
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  ungroup()

dem_inout
in_out_state total_dollars pct_of_total
IN 4423403 3.9
OUT 109563554 96.1

GOP overall totals

gop_inout <- winred_contribs %>%
  group_by(in_out_state) %>%
  summarise(total_dollars = sum(contribution_amount)) %>%
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  ungroup()

gop_inout
in_out_state total_dollars pct_of_total
IN 4364619 7.9
OUT 51224906 92.1

Dem Totals By State, Candidate

Dem totals by top states

dem_bystate <- actblue_contribs %>% 
  group_by(contributor_state) %>% 
  summarise(total_dollars = sum(contribution_amount)) %>% 
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  arrange(desc(total_dollars)) %>% 
  ungroup() %>% 
  head(15)

dem_bystate
contributor_state total_dollars pct_of_total
CA 26310973 23.1
NY 10755274 9.4
WA 6201746 5.4
MA 6000091 5.3
TX 4955258 4.3
FL 4790404 4.2
GA 4423403 3.9
IL 4216143 3.7
PA 3607792 3.2
MD 3374599 3.0
VA 3305815 2.9
CO 3059633 2.7
OR 2947936 2.6
NJ 2846614 2.5
NC 2317830 2.0

Dem candidate totals

dem_inout_bycand <- actblue_contribs %>% 
  group_by(ga_candidate, in_out_state) %>% 
  summarise(total_dollars = sum(contribution_amount)) %>% 
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  ungroup()

dem_inout_bycand
ga_candidate in_out_state total_dollars pct_of_total
OSSOFF IN 1988232 3.6
OSSOFF OUT 53777875 96.4
WARNOCK IN 2435171 4.2
WARNOCK OUT 55785679 95.8 
#chart it out
d <- ggplot(dem_inout, aes(in_out_state, total_dollars)) + geom_col(fill = "darkblue") + coord_flip() +
  theme_minimal()

#add extra elements to the chart and convert to ggplotly
d2 <- d + labs(title = "Dems: In vs. Out of State",
               subtitle = "(Since Nov. 4th)",
               caption = "Source: Bloomberg Analysis, FEC",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) 

d2

# The palette:
cbPalette <- c("#508104","#db8200")

d <- ggplot(data = dem_inout_bycand, aes(x = ga_candidate, y = total_dollars, fill = in_out_state)) +
  geom_col(position = "dodge") + coord_flip() + theme_minimal()

d2 <- d + labs(title="Dems Candidates: In vs. Out of State",
               subtitle = "(Since Nov. 4th)",
               caption = "Source: Source: Bloomberg Analysis, FEC",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) +
  scale_fill_manual(values=cbPalette) +
  theme(legend.title=element_blank())

d2

GOP Totals By State, Candidate

GOP totals by top states

gop_bystate <- winred_contribs %>% 
  group_by(contributor_state) %>% 
  summarise(total_dollars = sum(contribution_amount)) %>% 
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  arrange(desc(total_dollars)) %>% 
  ungroup() %>% 
  head(15)

gop_bystate
contributor_state total_dollars pct_of_total
CA 6776895 12.2
TX 6572833 11.8
FL 6460651 11.6
GA 4364619 7.9
NY 2479220 4.5
VA 1890726 3.4
IL 1650256 3.0
AZ 1547167 2.8
PA 1469174 2.6
NC 1389294 2.5
CO 1386772 2.5
WA 1329598 2.4
OH 1201732 2.2
NJ 1131895 2.0
MI 1090944 2.0

GOP candidate totals

gop_inout_bycand <- winred_contribs %>%
  group_by(ga_candidate, in_out_state) %>%
  summarise(total_dollars = sum(contribution_amount)) %>%
  mutate(
    pct_of_total = round_half_up(total_dollars/sum(total_dollars)*100, 1)
    ) %>% 
  ungroup()

gop_inout_bycand
ga_candidate in_out_state total_dollars pct_of_total
LOEFFLER IN 1981588 7.3
LOEFFLER OUT 25014065 92.7
PERDUE IN 2383031 8.3
PERDUE OUT 26210841 91.7 
#chart it out
d <- ggplot(gop_inout, aes(in_out_state, total_dollars)) + geom_col(fill = "darkred") + coord_flip() +
  theme_minimal()

#add extra elements to the chart and convert to ggplotly
d2 <- d + labs(title = "Republicans: In vs. Out of State",
               subtitle = "(Since Nov. 4th)",
               caption = "Source: Bloomberg Analysis, FEC",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar)

d2

# The palette:
cbPalette <- c("#508104","#db8200")

d <- ggplot(data = gop_inout_bycand, aes(x = ga_candidate, y = total_dollars, fill = in_out_state)) +
  geom_col(position = "dodge") + coord_flip() + theme_minimal()

d2 <- d + labs(title="Republican Candidates: In vs. Out of State",
               subtitle = "(Since Nov. 4th)",
               caption = "Source: Source: Bloomberg Analysis, FEC",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) +
  scale_fill_manual(values=cbPalette) +
  theme(legend.title=element_blank())

d2

Zip Codes

Top Zips for Democratic Total Contributions (ActBlue)

#pull out top 10 zips for the dems
dem10 <- zipcompare %>% 
  arrange(desc(demtotal)) %>% 
  head(15)

# dem10

#reorder factor to allow for descending bars
dem10 <- dem10 %>%
  mutate(zipname = fct_reorder(zipname, demtotal)) 

#export to file
write_csv(dem10, "output/dem10.csv")


#chart it out
d <- ggplot(dem10, aes(zipname, demtotal)) + geom_col(fill = "darkblue") + coord_flip() +
  theme_minimal()

#add extra elements to the chart and convert to ggplotly
d2 <- d + labs(title="GA Runoffs: Top ActBlue zip codes",
               # subtitle = "A subtitle",
               caption = "Source: FEC, Bloomberg Analysis",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) 

d2

# dd <- ggplotly(d2) 
# 
# dd_nomenu <- dd %>% config(displayModeBar = FALSE)
# dd_nomenu
# 
# #save as embeddable format
# # htmlwidgets::saveWidget(frameableWidget(dd), 'demtopzip_plt.html')
# htmlwidgets::saveWidget(frameableWidget(dd_nomenu), 'demtopzip_chart_nm.html')
# 
# #save as RDS object
# saveRDS(dd_nomenu, "zip_data/demtopzip_chart_nm.rds")

Top Zips for Democratic Advantage (ActBlue)

#pull out top zips for the dems
dem10_adv <- zipcompare %>% 
  filter(winner == "D") %>% 
  arrange(desc(advantage)) %>% 
  head(15)

#reorder factor to allow for descending bars
dem10_adv <- dem10_adv %>%
  mutate(zipname = fct_reorder(zipname, advantage)) 

#export to file
write_csv(dem10_adv, "output/dem10_adv.csv")


#chart it out
d <- ggplot(dem10_adv, aes(zipname, advantage)) + geom_col(fill = "darkblue") + coord_flip() +
  theme_minimal()

#add extra elements to the chart and convert to ggplotly
d2 <- d + labs(title="GA Runoffs: Top Zips Where Grassroots Dems Outraised GOP",
               subtitle = "(bars show advantage in dollars raised)",
               caption = "Source: FEC, Bloomberg Analysis",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) 

d2

Top Zips for Republican Total Contributions (WinRed)

#pull out top 10 zips for the gop
gop10 <- zipcompare %>% 
  arrange(desc(goptotal)) %>% 
  head(15)

# gop10

#reorder factor to allow for descending bars
gop10 <- gop10 %>%
  mutate(zipname = fct_reorder(zipname, goptotal)) 

#export to file
write_csv(gop10, "output/gop10.csv")


#chart it out
p <- ggplot(gop10, aes(zipname, goptotal)) + geom_col(fill = "darkred") + coord_flip() +
  theme_minimal()

#add titles and other extras
p2 <- p + labs(title="Ga Senate Runoffs: Top WinRed zip codes",
               # subtitle = "A subtitle",
               caption = "Source: FEC, Bloomberg Analysis",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) 

p2

# pp <- ggplotly(p2) 
# 
# pp
# 
# pp_nomenu <- pp %>% config(displayModeBar = FALSE)
# pp_nomenu
# 
# #save as embeddable format
# # htmlwidgets::saveWidget(frameableWidget(pp), 'goptopzip_plt.html')
# htmlwidgets::saveWidget(frameableWidget(pp_nomenu), 'goptopzip_plt_nm.html')

#save as RDS object
# saveRDS(pp_nomenu, "zip_data/goptopzip_plt_nm.rds")

Top Zips for Republican Advantage (WinRed)

#pull out top zips 
gop10_adv <- zipcompare %>% 
  filter(winner == "R") %>% 
  arrange(desc(advantage)) %>% 
  head(15)

#reorder factor to allow for descending bars
gop10_adv <- gop10_adv %>%
  mutate(zipname = fct_reorder(zipname, advantage)) 

#export to file
write_csv(gop10_adv, "output/gop10_adv.csv")


#chart it out
d <- ggplot(gop10_adv, aes(zipname, advantage)) + geom_col(fill = "darkred") + coord_flip() +
  theme_minimal()

#add extra elements to the chart and convert to ggplotly
d2 <- d + labs(title="GA Runoffs: Top Zips Where Grassroots GOP Outraised Dems",
               subtitle = "(bars show advantage in dollars raised)",
               caption = "Source: FEC, Bloomberg Analysis",
               x ="", y = "") +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(plot.subtitle = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_y_continuous(labels=dollar) 

d2

Zip Maps

#bring in comparison table created above
zipcompare <- readRDS("zip_data/zipcompare.rds")

head(zipcompare)
zip_code demtotal goptotal winner advantage city state zipname num_returns total_agi_value avg_agi avg_agi_rank
10024 461941.6 21277.16 D 440664.4 New York NY 10024 (New York, NY) 30410 9984610 328333.1 147
10025 432180.5 23949.47 D 408231.1 New York NY 10025 (New York, NY) 45610 6826320 149667.2 846
94114 413873.9 4116.66 D 409757.2 San Francisco CA 94114 (San Francisco, CA) 20360 4324445 212399.1 377
94110 405725.0 2744.16 D 402980.9 San Francisco CA 94110 (San Francisco, CA) 38770 4590850 118412.4 1460
10023 395714.1 53242.10 D 342472.0 New York NY 10023 (New York, NY) 34070 11766656 345367.1 125
94117 328673.8 2513.33 D 326160.5 San Francisco CA 94117 (San Francisco, CA) 23140 3644629 157503.4 748 
names(zipcompare)
##  [1] "zip_code"        "demtotal"        "goptotal"        "winner"         
##  [5] "advantage"       "city"            "state"           "zipname"        
##  [9] "num_returns"     "total_agi_value" "avg_agi"         "avg_agi_rank"
zipcompare$GEOID <- zipcompare$zip_code

#remove any negative values
zipcompare <- zipcompare %>% 
  filter(demtotal >= 0,
         goptotal >= 0)

zipcompare <- zipcompare %>% 
  filter(!str_detect(GEOID, "^99"),
         !str_detect(GEOID, "^96"),
         !str_detect(GEOID, "^006"),
         !str_detect(GEOID, "^007"),
         !str_detect(GEOID, "^009")
           )


#### get zip code lat/long points from census gazeteer file
zip_points <- read_csv("zip_data/zip_points.csv", 
                       col_types = cols(ALAND = col_skip(), 
                                        ALAND_SQMI = col_skip(), AWATER = col_skip(), 
                                        AWATER_SQMI = col_skip()))

colnames(zip_points) <- c("GEOID", "lat", "lon")

#join data
zip_map <- inner_join(zipcompare, zip_points)
## Joining, by = "GEOID"
zip_map$winner <- as.factor(zip_map$winner)

#add dollar formatting
zip_map$demdisplay <- dollar(zip_map$demtotal)
zip_map$gopdisplay <- dollar(zip_map$goptotal)

National zipcode map of Dem vs. GOP advantage in contributions

#### MAPPING POINTS ##### ---------------------------------------

factpal <- colorFactor(c("blue","red"), zip_map$winner)

#labels
labs1 <- lapply(seq(nrow(zip_map)), function(i) {
  paste0( '<p>', 'Zip code: ', '<strong>', zip_map[i, "GEOID"], '</strong></p>',
          '<p></p>', 
          "Democrats: ", zip_map[i, "demdisplay"],
          '<p></p>', 
          "Republicans: ", zip_map[i, "gopdisplay"]
  ) 
})

m1 <- leaflet(zip_map) %>% 
  addTiles() %>%
  addCircles(lng = ~lon, lat = ~lat, weight = .4,
             stroke = FALSE, fillOpacity = .25,
             radius = ~sqrt(advantage) * 300, 
             fillColor = ~factpal(winner),
             label = lapply(labs1, HTML)
  ) %>%
  addControl("ActBlue vs. WinRed - GA Senate candidate contributions by zip code", position = "topright") 
# %>% 
#   setView(-96, 37.8, zoom=4) 

m1
#save to frameable file for CMS
htmlwidgets::saveWidget(frameableWidget(m1),'gasenate_actblue_vs_winred_zippoints.html')

# save as rds object
saveRDS(m1, "savedmap_zipcompare.rds")

Atlanta-area-only zipcode map of Dem vs. GOP advantage in contributions

#filter for just GA
zip_map <- zip_map %>% 
  filter(state == "GA")

factpal <- colorFactor(c("blue","red"), zip_map$winner)

#labels
labs1 <- lapply(seq(nrow(zip_map)), function(i) {
  paste0( '<p>', 'Zip code: ', '<strong>', zip_map[i, "GEOID"], '</strong></p>',
          '<p></p>', 
          "Democrats: ", zip_map[i, "demdisplay"],
          '<p></p>', 
          "Republicans: ", zip_map[i, "gopdisplay"]
  ) 
})

m2 <- leaflet(zip_map) %>% 
  addTiles() %>%
  addCircles(lng = ~lon, lat = ~lat, weight = .4,
             stroke = FALSE, fillOpacity = .25,
             radius = ~sqrt(advantage) * 20, 
             fillColor = ~factpal(winner),
             label = lapply(labs1, HTML)
  ) %>%
  addControl("ActBlue vs. WinRed - GA Senate candidate contributions by zip code", position = "topright") %>%
  setView(-84.3880, 33.7490, zoom = 9)

m2
#save to frameable file for CMS
htmlwidgets::saveWidget(frameableWidget(m2),'gasenate_actblue_vs_winred_zippoints_ATLONLY.html')

# save as rds object
saveRDS(m2, "savedmap_zipcompare_ATLONLY.rds")
#### top zips for each party

t_gop <- zipcompare %>% 
  select(zip_code, total = goptotal) %>%
  mutate(party = "R") %>% 
  arrange(desc(total)) %>% 
  head(250)

t_dem <- zipcompare %>% 
  select(zip_code, total = demtotal) %>% 
  mutate(party = "D") %>% 
  arrange(desc(total)) %>% 
  head(250)

#combine
t_both <- bind_rows(t_dem, t_gop)

t_both %>% write_xlsx("output/t_both.xlsx")